SandboxAQ

SandboxAQ

ML Research Scientist, Co-Folding and Affinity

United States · Senior

Sponsorship not specifiedDetected 42 days ago
PythonAWSGCPAzureCloud PlatformsMachine LearningDeep LearningPyTorchData AnalysisNLPEpicBioinformaticsResearchCommunicationCollaboration

About the role

  • We are a flexible, creative, and impact driven team of multidisciplinary scientists and engineers, whose products dramatically accelerate the creation of molecules and medicines.
  • Working alongside a high-performing team of scientists and engineers, you will help advance the state-of-the-art in protein-ligand co-folding, translating cutting-edge research into scalable workflows that power our drug discovery software.

Responsibilities

  • Drive Rigorous Benchmarking: Design and execute systematic evaluation pipelines to measure model performance against state-of-the-art methods and internal benchmarks.
  • Contribute to Research-to-Product Pipelines: Collaborate with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows.
  • Collaborate Across Teams: Work closely with multidisciplinary teams - including ML engineers, structural biologists, and software engineers - to prototype and scale impactful solutions.
  • Scientific Rigor: Demonstrated ability to design controlled experiments, interpret results critically, and iterate effectively on model development.
  • Postdoctoral Experience: Active or recently completed postdoctoral research in co-folding, structure-based drug design, or a closely related computational domain.
  • Biopharma Context: Familiarity with drug discovery workflows, including hit identification, lead optimization, or structure-based drug design (SBDD).

Requirements

  • Direct experience with protein structure prediction or protein-ligand co-folding methods (e.g., AlphaFold2/3, RoseTTAFold, Chai-1, Boltz, or comparable systems), developed through graduate or postdoctoral research.
  • Strong proficiency in Python and modern ML frameworks (PyTorch and/or JAX).
  • ability to work collaboratively in a fast-paced, multidisciplinary research environment.
  • Familiarity with binding affinity prediction methods, including structure-based or physics-informed approaches.
  • Programming Proficiency: Strong proficiency in Python and modern ML frameworks (PyTorch and/or JAX).

Skills

  • Familiarity with agentic coding tools (e.g., Claude Code, Codex) to accelerate research prototyping.
  • Experience deploying ML workflows on public cloud infrastructure (e.g., GCP, AWS, or Azure).
  • WHY JOIN US?
  • Guidance for candidates on using AI Tools in interviews https://www.sandboxaq.com/ai-in-interviews

Compensation

  • Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.

Benefits

  • Work-Life Balance: Flexible paid time off, company-wide seasonal breaks, and support for flexible work arrangements that enable sustainable performance.
  • Career Development: Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.
  • Develop and Iterate on Co-Folding Models: Implement, experiment with, and refine deep learning models for protein-ligand co-folding and structure prediction, building on the latest research from the field.
  • We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.

Company info

  • SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges.
  • The company's Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
  • We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties.
  • The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
  • At SandboxAQ, we've cultivated an environment that encourages creativity, collaboration, and impact.
  • By investing deeply in our people, we're building a thriving, global workforce poised to tackle the world's epic challenges.
  • Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
  • The AI Sim R&D team creates leading edge ML and physics-based models ("LQMs") to advance drug and materials discovery.
  • As a Research Scientist focusing on Co-Folding & Affinity, you will contribute to building SandboxAQ's next generation of structure prediction and binding affinity models.
  • This is an opportunity to do frontier science with real-world impact - developing models that redefine what's possible in computational drug discovery.
  • Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.
  • We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued.

Equal opportunity

  • All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.
  • Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles.
  • If you need such an accommodation, please let a member of our Recruiting team know.
  • Read: Guidance for candidates on using AI Tools in interviews https://www.sandboxaq.com/ai-in-interviews

Visa & Work Authorization

  • ill receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status
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This listing is sourced directly from SandboxAQ's careers page and normalized into a canonical job model.